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The GH-300 practice test trains you in foundational-level AI pair-programming concepts with GitHub Copilot across the GitHub ecosystem.
Why should I take the GH-300 exam?
The GH-300 certification focuses on Copilot as the centerpiece of modern software delivery. It helps you demonstrate practical skills in crafting effective prompts, evaluating and adapting suggestions, using Copilot Chat, and integrating Copilot into everyday workflows, while applying responsible-use practices (privacy, security, and compliance), configuring organization and repository-level Copilot controls (e.g., suggestion filtering and policies), and understanding Copilot's limitations. The GH-300 exam is also an optional and recommended stepping stone for broader GitHub and AI-DevOps certification paths, including associate, expert, and specialist-level credentials.
The GH-300 practice test includes two different modes: certification and practice mode. Certification mode allows you to assess your knowledge and discover your weak areas, with practice mode allowing you to focus on the areas that need development.
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Release Date: 09/2025
Job Role: Software Developer
Language: English
The GH-300 practice test contains 126 questions and covers the following objectives:
Domain 1: Responsible AI - 8 questions
Explain responsible usage of AI
- Describe the risks associated with using AI
- Explain the limitations of using generative AI tools (depth of the source data for the model, bias in the data, etc.)
- Explain the need to validate the output of AI tools
- Identify how to operate a responsible AI
- Identify the potential harms of generative AI (bias, secure code, fairness, privacy, transparency)
- Explain how to mitigate the occurrence of potential harms
- Explain ethical AI
Domain 2: GitHub Copilot plans and features - 37 questions
Identify the different GitHub Copilot plans
- Understand the differences between Copilot Individual, Copilot Business, Copilot Enterprise, and Copilot Business for non-GHE
- Understand Copilot for non-GitHub customers
- Define GitHub Copilot in the IDE
- Define GitHub Copilot Chat in the IDE
- Describe the different ways to trigger GitHub Copilot (chat, inline chat, suggestions, multiple suggestions, exception handling, CLI)
Identify the main features with GitHub Copilot Individual
- Explain the difference between GitHub Copilot Individual and GitHub Copilot Business (data exclusions, IP indemnity, billing, etc.)
- Understand the available features in the IDE for GitHub Copilot Individual
Identify the main features of GitHub Copilot Business
- Demonstrate how to exclude specific files from GitHub Copilot
- Demonstrate how to establish organization-wide policy management
- Describe the purpose of organization audit logs for GitHub Copilot Business
- Explain how to search audit log events for GitHub Copilot Business
- Explain how to manage GitHub Copilot Business subscriptions via the REST API
Identify the main features with GitHub Copilot Chat
- Identify the use cases where GitHub Copilot Chat is most effective
- Explain how to improve performance for GitHub Copilot Chat
- Identify the limitations of using GitHub Copilot Chat
- Identify the available options for using code suggestions from GitHub Copilot Chat
- Explain how to share feedback about GitHub Copilot Chat
- Identify the common best practices for using GitHub Copilot Chat
- Identify the available slash commands when using GitHub Copilot Chat
Identify the main features with GitHub Copilot Enterprise
- Explain the benefits of using GitHub Copilot Chat on GitHub.com
- Explain GitHub Copilot pull request summaries
- Explain how to configure and use Knowledge Bases within GitHub Copilot Enterprise
- Describe the different types of knowledge that can be stored in a Knowledge Base (e.g., code snippets, best practices, design patterns)
- Explain the benefits of using Knowledge Bases for code completion and review (e.g., improve code quality, consistency, and efficiency)
- Describe instructions for creating, managing, and searching Knowledge Bases within GitHub Copilot Enterprise, including details on indexing and other relevant configuration steps
- Explain the benefits of using custom models
Using GitHub Copilot in the CLI
- Discuss the steps for installing GitHub Copilot in the CLI
- Identify the common commands when using GitHub Copilot in the CLI
- Identify the multiple settings you can configure within GitHub Copilot in the CLI
Domain 3: How GitHub Copilot works and handles data - 18 questions
Describe the data pipeline lifecycle of GitHub Copilot code suggestions in the IDE
- Visualize the lifecycle of a GitHub Copilot code suggestion
- Explain how GitHub Copilot gathers context
- Explain how GitHub Copilot builds a prompt
- Describe the proxy service and the filters each prompt goes through
- Describe how the large language model produces its response
- Explain the post-processing of GitHub Copilot’s responses through the proxy server
- Identify how GitHub Copilot identifies matching code
Describe how GitHub Copilot handles data
- Describe how the data in GitHub Copilot individual is used and shared
- Explain the data flow for GitHub Copilot code completion
- Explain the data flow for GitHub Copilot Chat
- Describe the different types of input processing for GitHub Copilot Chat (types of prompts it was designed for)
Describe the limitations of GitHub Copilot (and LLMs in general)
- Describe the effect of most seen examples on the source data
- Describe the age of code suggestions (how old and relevant the data is)
- Describe the nature of GitHub Copilot providing reasoning and context from a prompt vs calculations
- Describe limited context windows
Domain 4: Prompt Crafting and Prompt Engineering - 11 questions
Describe the fundamentals of prompt crafting
- Describe how the context for the prompt is determined
- Describe the language options for promoting GitHub Copilot
- Describe the different parts of a prompt
- Describe the role of prompting
- Describe the difference between zero-shot and few-shot prompting
- Describe the way chat history is used with GitHub Copilot
- Identify prompt crafting best practices when using GitHub Copilot
Describe the fundamentals of prompt engineering
- Explain prompt engineering principles, training methods, and best practices
- Describe the prompt process flow
Domain 5: Developer use cases for AI - 19 questions
Improve developer productivity
- Describe how AI can improve common use cases for developer productivity
- Learning new programming languages and frameworks
- Language translation
- Context switching
- Writing documentation
- Personalized context-aware responses
- Generating sample data
- Modernizing legacy applications
- Debugging code
- Data science
- Code refactoring
- Discuss how GitHub Copilot can help with SDLC (Software Development Lifecycle) management
- Describe the limitations of using GitHub Copilot
- Describe how to use the productivity API to see how GitHub Copilot impacts coding
Domain 6: Testing with GitHub Copilot - 10 questions
Describe the options for generating testing for your code
- Describe how GitHub Copilot can be used to add unit tests, integration tests, and other test types to your code
- Explain how GitHub Copilot can assist in identifying edge cases and suggesting tests to address them
Describe the different SKUs for GitHub Copilot
- Describe the different SKUs and the privacy considerations for GitHub Copilot
- Describe the different code suggestion configuration options on the organization level
- Describe the GitHub Copilot Editor config file
Domain 7: Privacy fundamentals and context exclusions - 23 questions
Enhance code quality through testing
- Describe how to improve the effectiveness of existing tests with GitHub Copilot’s suggestions
- Describe how to generate boilerplate code for various test types using GitHub Copilot
- Explain how GitHub Copilot can help write assertions for different testing scenarios
Leverage GitHub Copilot for security and performance
- Describe how GitHub Copilot can learn from existing tests to suggest improvements and identify potential issues in the code
- Explain how to use GitHub Copilot Enterprise for collaborative code reviews, leveraging security best practices, and performance considerations
- Explain how GitHub Copilot can identify potential security vulnerabilities in your code
- Describe how GitHub Copilot can suggest code optimizations for improved performance
Identify content exclusions
- Describe how to configure content exclusions in a repository and organization
- Explain the effects of content exclusions
- Explain the limitations of content exclusions
- Describe the ownership of GitHub Copilot outputs
Safeguards
- Describe the duplication detector filter
- Explain contractual protection
- Explain how to configure GitHub Copilot settings on GitHub.com
- Enabling/disabling duplication detection
- Enabling/disabling prompt and suggestion collection
- Describe security checks and warnings
Troubleshooting
- Explain how to solve the issue if code suggestions are not showing in your editor for some files
- Explain why context exclusions may not be applied
- Explain how to trigger GitHub Copilot when suggestions are either absent or not ideal
- Explain steps for context exclusions in code editors
System Requirements
A practice test simulates the actual test and aims to provide you with optimal preparation for what to expect on the real exam. A MeasureUp practice test includes around 150 questions covering the exam objective domains. There are two possible test-taking modes to prepare students for their certification: Certification Mode and Practice Mode.
- Practice Mode allows users to highly customize their testing environment. They may select how many questions to include, the maximum time to finish, randomize question order, and choose how and which questions are shown.
- Certification Mode simulates the actual testing environment. It is timed and does not permit users to view answers or explanations until after the test.
How does it work?
Check out our video to see exactly how MeasureUp’s practice tests work.
Why should you trust MeasureUp over free learning material?
| MeasureUp | Free learning material |
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Will studying with a MeasureUp practice test improve my chances of passing at the first attempt?
Yes. MeasureUp's practice tests are designed to help you save time and pass on your first attempt. The test is fully customizable, allowing you to focus on your weak areas. Since the style, objectives, question types, and difficulty match the official exam, passing the practice test twice consecutively in Certification Mode means you're exam ready.
What can I expect to earn if I pass the GH-300 exam?
On passing the GH-300 exam and obtaining a job as a software developer, you can expect to earn a salary in the United States of approximately 120,000 USD.
Source: Indeed
Continue growing with MeasureUp’s learning material. Explore the GitHub learning path.
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GH-300 PRACTICE TEST
Why should you use our GH-300 practice test?
The MeasureUp GH-300 practice test is the most realistic simulation of the actual certification exam on the market, offering you the perfect opportunity to pass the official exam on the first attempt. And with our Test Pass Guarantee, you can be sure of success! The GH-300: GitHub copilot practice test has been created by leading experts in the field.
Why should you trust the Microsoft GH-300 Practice Test from MeasureUp over free learning material?
The MeasureUp GH-300 practice test has many advantages over free learning material, including:
- A greater number of questions, so more opportunities to learn.
- Detailed explanations with online references of correct and incorrect answers.
- A total of fourteen different question types, recreating the look and feel of the real exam.
- Customizable based on your needs. Certification & Practice Modes.
- Test Pass Guarantee.
- Written, reviewed, and edited by experts.
How to use the GH-300 Practice Test?
You can use the GH-300 practice test in two different modes: certification and practice mode. The former gives you the possibility to assess your knowledge and discover your weak areas, and the latter allows you to focus on these areas, ensuring you spend your time wisely. We recommend you initially take the GH-300 practice test in certification mode. By studying the generated report on completing the test, you will get a helpful overview of which areas require further attention.
You should then take the test in practice mode in order to develop those areas. Once you are confident you have improved your knowledge in those areas, you can re-take the test in certification mode and, on passing 3 consecutive times with a score of 90%, you know you are exam ready!
Will the questions be the same as the actual exam?
Although the questions will emulate those of the official exam in terms of style, content, and level of difficulty, for reasons of copyright they will not be identical. This will enable you to fully understand the content you are studying so that, no matter how the questions are focused, you can be confident you are covering the same material and that you will have no issues in passing the exam.
Microsoft GH-300 CERTIFICATION EXAM
What is the GH-300?
The GH-300 is a certification exam designed to validate foundational knowledge and practical skills in computer hardware, mobile devices, networking, virtualization, and cloud computing. The GitHub Copilot certification serves as an entry-level credential for individuals starting their career in IT, demonstrating their ability to support and troubleshoot modern technology environments.
Is the GH-300: GitHub Copilot exam hard?
The GH-300: GitHub Copilot exam can be moderately challenging, depending on your familiarity with coding, AI-assisted development, and GitHub workflows. For experienced developers who regularly use Copilot and understand its strengths and limitations, the exam may feel straightforward. However, for beginners or those new to AI pair-programming tools, it could be difficult, as it tests both technical coding knowledge and practical application of Copilot in real-world scenarios.
How can you pass the GH-300 certification exam?
- Review the GH-300 exam domains carefully.
- Create your study plan for your preparation.
- Enroll for the MeasureUp practice tests. Our practice tests simulate the actual exam in terms of style, format, skill sets, question structure, and level of difficulty, and can be taken either in practice mode or certification mode.
- Practice, practice, practice! When you have reviewed all the questions available in the GH-300 practice test, including checking the correct answers, reviewing the explanations regarding all the different answer options, and consulting the carefully chosen references, it is now time to take the test in Certification Mode. This is the closest experience you’ll get to the actual exam. And when you pass the Certification Mode twice consecutively with a score of 90% or more, you know you are… Exam ready!
How many questions does GH-300 have?
There are a maximum of 90 questions on the Microsoft GH-300 exam.
Is the GH-300 worth it?
Yes, the GH-300 certification is worth it if you want to showcase your ability to work efficiently with AI-assisted coding tools like GitHub Copilot. It demonstrates to employers and peers that you can integrate modern AI development practices into real-world projects, boosting productivity and code quality. For developers looking to stay ahead in the evolving software landscape, earning the GH-300 can provide a valuable career edge.
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